Vision Transformer Basics

An introduction to the use of transformers in Computer vision.

Timestamps:
00:00 - Vision Transformer Basics
01:06 - Why Care about Neural Network Architectures?
02:40 - Attention is all you need
03:56 - What is a Transformer?
05:16 - ViT: Vision Transformer (Encoder-Only)
06:50 - Transformer Encoder
08:04 - Single-Head Attention
11:45 - Multi-Head Attention
13:36 - Multi-Layer Perceptron
14:45 - Residual Connections
16:31 - LayerNorm
18:14 - Position Embeddings
20:25 - Cross/Causal Attention
22:14 - Scaling Up
23:03 - Scaling Up Further
23:34 - What factors are enabling effective further scaling?
24:29 - The importance of scale
26:04 - Transformer scaling laws for natural language
27:00 - Transformer scaling laws for natural language (cont.)
27:54 - Scaling Vision Transformer
29:44 - Vision Transformer and Learned Locality

Topics: #computervision #ai #introduction

Notes:
This lecture was given as part of the 2022/2023 4F12 course at the University of Cambridge.

Links:

References for papers mentioned in the video can be found at

For related content:

[rldp]

This is one of the best explanations of not just ViT, but transformers in general that I have watched. Excellent video

[whale]

Unbelievable quality. Happy to be here before this channel blows up.

[srinjoy.bhuiya]

One of the greatest explanations of the concepts of transformers to a Computer Vision Reserach

[capsbr]

Goodness, what a remarkable video. This is by far the best explanation video I have watched about vision transformers.

[thetechnocrack]

This is one of the cleanest explanation of ViTs I have come across. Amazing work Samuel! Inspiring.

[continuallearning]

Excellent video! Honored to be here before it goes viral 🙏🏾

[ShravanKumar]

Beautifully put together. Keep it going @Sam

[iygqydp]

Thank you for making this wonderful video. So clear! Please continue your awesome video work!

[PotatoKaboom]

I've held guest lectures on the inner workings of transformers myself, but I still learned a bunch from this! Everything after 22:15 was very exciting to watch, very well presented and easy to understand! Very well done, I dubscribed for more :)

[piclkesthedrummer]

This is by far one of the most accurate, yet understandable and intuitive explaination of such a hard concept, you did a better job at explaining it than the authors! very impressive!

[jesusalpaca]

for a beginner like me, I would say, this is the introduce video that we were waiting for :')

[thecheekychinaman]

I was studying up on Transformers and ViTs half a year ago, and recently checked back to find this (to my surprise). Great clear explanations, can tell CAML is in great hands!

[fvojqkl]

🎯 Key Takeaways for quick navigation:

00:00 🧠 *The Evolution of AI and Computer Vision*
- General methods leveraging computation prove most effective in AI development.
- Evolution from handcrafted features to Convolutional Neural Networks (CNNs) and then to Transformers, showcasing a reduction in inductive biases and an increase in data-driven approaches.
01:09 🤖 *Neural Network Architectures*
- Importance of network architecture in building intelligent machines.
- Distinction between network architecture and network parameters, focusing on resource limitations and efficient design.
02:32 💡 *Introduction to Transformers*
- Transformers' dominance in AI, initially in Natural Language Processing (NLP) and then in Computer Vision.
- Discussion on why Transformers took time to transition from NLP to Computer Vision.
03:57 🌐 *Understanding Transformers: Encoder and Decoder*
- Explanation of the Transformer architecture with its encoder and decoder components.
- Different variants of Transformers: Encoder-only, Decoder-only, and Encoder-Decoder architectures.
05:33 🔍 *Applying Transformers to Computer Vision*
- Vision Transformers (ViT) process images by slicing them into patches, using position embeddings and Transformer encoders.
- The methodology of transforming images into a sequence of embeddings for the Transformer encoder.
07:08 🔗 *Multi-Head Attention in Transformers*
- Detailed explanation of the multi-head attention mechanism in Transformers.
- Role of queries, keys, and values in facilitating communication between different embeddings.
09:12 🧩 *Transformer Encoder Blocks and Scaling*
- The structure and function of Transformer encoder blocks, including multi-head attention and MLP.
- Importance of residual connections and layer normalization in optimizing Transformer models.
11:05 🚀 *Scaling and Hardware Influence in AI*
- The impact of scaling and hardware advancements on Transformer model performance.
- Discussion on the exponential increase in computational resources for training large models.
13:50 🛠 *MLP and Optimization in Transformers*
- Role of the multi-layer perceptron (MLP) in Transformer architecture for independent processing of embeddings.
- Importance of non-linearities like ReLU and GELU in Transformer models.
15:00 ⚙️ *Residual Connections and Layer Normalization*
- Implementation and significance of residual connections and layer normalization in Transformers.
- These components facilitate gradient flow and stable learning in deep network training.
17:05 🌐 *Positional Embeddings in Transformers*
- Explanation of positional embeddings in Transformers, necessary for maintaining spatial information in sequences.
- Different methods of implementing positional embeddings in Transformer models.
19:27 🔄 *Cross Attention and Causal Attention in Transformers*
- Discussion of

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[abhimanyuyadav]

Your weekly ai news was really useful
Please bring it back

[amoghjain]

Thank you so very much for sharing your insights and intuition behind soooo many concepts.

[mattsong]

Thanks for such a informative and educational video

[vil]

Wow, this video helped me a lot in understanding Attention and ViT. Packed with all the logics needed to design a solution using the latest as of this day.

[aminkarimi]

The best video to easily understand VIT

[rmmajor]

That is a masterpiece of a video! Many thanks for your work!

[soylentpink]

Very good video - contents & it’s presentation!